{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3c5d72f4",
   "metadata": {},
   "source": [
    "# DistilBERT Classifier as Feature Extractor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb9d0299-8fc0-48f0-9b02-4c19214d479a",
   "metadata": {},
   "source": [
    "In this feature-based approach, we are using the embeddings from a pretrained transformer to train a random forest and logistic regression model in scikit-learn:\n",
    "\n",
    "![](figures/feature-extractor.jpeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6fd9cda8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install transformers datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df18e3de-577a-43c5-8b9d-868397a6d7da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# conda install sklearn --yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "033b75c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch       : 1.12.1\n",
      "transformers: 4.23.1\n",
      "datasets    : 2.6.1\n",
      "sklearn     : 0.0\n",
      "\n",
      "conda environment: dl-fundamentals\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark --conda -p torch,transformers,datasets,sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "602ba8a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cfd724d",
   "metadata": {},
   "source": [
    "# 1 Loading the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd06d930",
   "metadata": {},
   "source": [
    "The IMDB movie review dataset consists of 50k movie reviews with sentiment label (0: negative, 1: positive)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60fe0b76",
   "metadata": {},
   "source": [
    "## 1a) Load from `datasets` Hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "447e24bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import list_datasets, load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2baf2f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# list_datasets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6310d5bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset imdb (/home/raschka/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "620d4c29e27a4082927c2102e80b2e66",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['text', 'label'],\n",
      "        num_rows: 25000\n",
      "    })\n",
      "    test: Dataset({\n",
      "        features: ['text', 'label'],\n",
      "        num_rows: 25000\n",
      "    })\n",
      "    unsupervised: Dataset({\n",
      "        features: ['text', 'label'],\n",
      "        num_rows: 50000\n",
      "    })\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "imdb_data = load_dataset(\"imdb\")\n",
    "print(imdb_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "552bbb2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': \"This film is terrible. You don't really need to read this review further. If you are planning on watching it, suffice to say - don't (unless you are studying how not to make a good movie).<br /><br />The acting is horrendous... serious amateur hour. Throughout the movie I thought that it was interesting that they found someone who speaks and looks like Michael Madsen, only to find out that it is actually him! A new low even for him!!<br /><br />The plot is terrible. People who claim that it is original or good have probably never seen a decent movie before. Even by the standard of Hollywood action flicks, this is a terrible movie.<br /><br />Don't watch it!!! Go for a jog instead - at least you won't feel like killing yourself.\",\n",
       " 'label': 0}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_data[\"train\"][99]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40bdb9c5",
   "metadata": {},
   "source": [
    "## 1b) Load from local directory"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9103ec2d",
   "metadata": {},
   "source": [
    "The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\n",
    "\n",
    "A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\n",
    "\n",
    "    tar -zxf aclImdb_v1.tar.gz\n",
    "\n",
    "B) If you are working with Windows, download an archiver such as 7Zip to extract the files from the download archive."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac508bb8",
   "metadata": {},
   "source": [
    "C) Use the following code to download and unzip the dataset via Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "241ecc96",
   "metadata": {},
   "source": [
    "**Download the movie reviews**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "02aeade4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import tarfile\n",
    "import time\n",
    "import urllib.request\n",
    "\n",
    "source = \"http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\"\n",
    "target = \"aclImdb_v1.tar.gz\"\n",
    "\n",
    "if os.path.exists(target):\n",
    "    os.remove(target)\n",
    "\n",
    "\n",
    "def reporthook(count, block_size, total_size):\n",
    "    global start_time\n",
    "    if count == 0:\n",
    "        start_time = time.time()\n",
    "        return\n",
    "    duration = time.time() - start_time\n",
    "    progress_size = int(count * block_size)\n",
    "    speed = progress_size / (1024.0**2 * duration)\n",
    "    percent = count * block_size * 100.0 / total_size\n",
    "\n",
    "    sys.stdout.write(\n",
    "        f\"\\r{int(percent)}% | {progress_size / (1024.**2):.2f} MB \"\n",
    "        f\"| {speed:.2f} MB/s | {duration:.2f} sec elapsed\"\n",
    "    )\n",
    "    sys.stdout.flush()\n",
    "\n",
    "\n",
    "if not os.path.isdir(\"aclImdb\") and not os.path.isfile(\"aclImdb_v1.tar.gz\"):\n",
    "    urllib.request.urlretrieve(source, target, reporthook)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2a867dcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isdir(\"aclImdb\"):\n",
    "\n",
    "    with tarfile.open(target, \"r:gz\") as tar:\n",
    "        tar.extractall()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9318d4d0",
   "metadata": {},
   "source": [
    "**Convert them to a pandas DataFrame and save them as CSV**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "464e587c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████| 50000/50000 [00:55<00:00, 901.12it/s]\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from packaging import version\n",
    "from tqdm import tqdm\n",
    "\n",
    "# change the `basepath` to the directory of the\n",
    "# unzipped movie dataset\n",
    "\n",
    "basepath = \"aclImdb\"\n",
    "\n",
    "labels = {\"pos\": 1, \"neg\": 0}\n",
    "\n",
    "df = pd.DataFrame()\n",
    "\n",
    "with tqdm(total=50000) as pbar:\n",
    "    for s in (\"test\", \"train\"):\n",
    "        for l in (\"pos\", \"neg\"):\n",
    "            path = os.path.join(basepath, s, l)\n",
    "            for file in sorted(os.listdir(path)):\n",
    "                with open(os.path.join(path, file), \"r\", encoding=\"utf-8\") as infile:\n",
    "                    txt = infile.read()\n",
    "\n",
    "                if version.parse(pd.__version__) >= version.parse(\"1.3.2\"):\n",
    "                    x = pd.DataFrame(\n",
    "                        [[txt, labels[l]]], columns=[\"review\", \"sentiment\"]\n",
    "                    )\n",
    "                    df = pd.concat([df, x], ignore_index=False)\n",
    "\n",
    "                else:\n",
    "                    df = df.append([[txt, labels[l]]], ignore_index=True)\n",
    "                pbar.update()\n",
    "df.columns = [\"text\", \"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "02649593",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.random.seed(0)\n",
    "df = df.reindex(np.random.permutation(df.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59ca0386",
   "metadata": {},
   "source": [
    "**Basic datasets analysis and sanity checks**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c2db547a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class distribution:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([25000, 25000])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Class distribution:\")\n",
    "np.bincount(df[\"label\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a007e612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 173.0, 2470)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_len = df[\"text\"].apply(lambda x: len(x.split()))\n",
    "text_len.min(), text_len.median(), text_len.max() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00f4b04d",
   "metadata": {},
   "source": [
    "**Split data into training, validation, and test sets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ff703901",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_shuffled = df.sample(frac=1, random_state=1).reset_index()\n",
    "\n",
    "df_train = df_shuffled.iloc[:35_000]\n",
    "df_val = df_shuffled.iloc[35_000:40_000]\n",
    "df_test = df_shuffled.iloc[40_000:]\n",
    "\n",
    "df_train.to_csv(\"train.csv\", index=False, encoding=\"utf-8\")\n",
    "df_val.to_csv(\"validation.csv\", index=False, encoding=\"utf-8\")\n",
    "df_test.to_csv(\"test.csv\", index=False, encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bd5f770",
   "metadata": {},
   "source": [
    "**Load the dataset via `load_dataset`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a1aa66c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default-3ba780b1f6e4b45e\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset csv/default to /home/raschka/.cache/huggingface/datasets/csv/default-3ba780b1f6e4b45e/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7083a3ff6d604445bf7e8ab46b848116",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "13a82b6c35834bbd88a824adc89c5085",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 tables [00:00, ? tables/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:714: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
      "  return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n",
      "/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pandas/io/common.py:122: ResourceWarning: unclosed file <_io.BufferedReader name='/home/raschka/scratch/deeplearning-models/pytorch_ipynb/transformer/train.csv'>\n",
      "  self.handle.detach()\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "0 tables [00:00, ? tables/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:714: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
      "  return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n",
      "/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pandas/io/common.py:122: ResourceWarning: unclosed file <_io.BufferedReader name='/home/raschka/scratch/deeplearning-models/pytorch_ipynb/transformer/validation.csv'>\n",
      "  self.handle.detach()\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
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       "0 tables [00:00, ? tables/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:714: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
      "  return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset csv downloaded and prepared to /home/raschka/.cache/huggingface/datasets/csv/default-3ba780b1f6e4b45e/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pandas/io/common.py:122: ResourceWarning: unclosed file <_io.BufferedReader name='/home/raschka/scratch/deeplearning-models/pytorch_ipynb/transformer/test.csv'>\n",
      "  self.handle.detach()\n",
      "ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "225d62e325d54b6b9d2d3d7a0da23f31",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['index', 'text', 'label'],\n",
      "        num_rows: 35000\n",
      "    })\n",
      "    validation: Dataset({\n",
      "        features: ['index', 'text', 'label'],\n",
      "        num_rows: 5000\n",
      "    })\n",
      "    test: Dataset({\n",
      "        features: ['index', 'text', 'label'],\n",
      "        num_rows: 10000\n",
      "    })\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "imdb_dataset = load_dataset(\n",
    "    \"csv\",\n",
    "    data_files={\n",
    "        \"train\": \"train.csv\",\n",
    "        \"validation\": \"validation.csv\",\n",
    "        \"test\": \"test.csv\",\n",
    "    },\n",
    ")\n",
    "\n",
    "print(imdb_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "846d83b1",
   "metadata": {},
   "source": [
    "# 2 Tokenization and Numericalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5ea762ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokenizer input max length: 512\n",
      "Tokenizer vocabulary size: 30522\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
    "print(\"Tokenizer input max length:\", tokenizer.model_max_length)\n",
    "print(\"Tokenizer vocabulary size:\", tokenizer.vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8432c15c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_text(batch):\n",
    "    return tokenizer(batch[\"text\"], truncation=True, padding=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0bb392cf",
   "metadata": {},
   "outputs": [
    {
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       "version_minor": 0
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    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ea231de3a0e4c5fbf6320f816e29b83",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "imdb_tokenized = imdb_dataset.map(tokenize_text, batched=True, batch_size=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6d4103c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "del imdb_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfeb1553",
   "metadata": {},
   "source": [
    "# 3 Using DistilBERT as a Feature Extractor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9f2c474d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_layer_norm.weight', 'vocab_transform.weight', 'vocab_projector.weight', 'vocab_projector.bias', 'vocab_transform.bias', 'vocab_layer_norm.bias']\n",
      "- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModel\n",
    "\n",
    "model = AutoModel.from_pretrained(\"distilbert-base-uncased\")\n",
    "model.to(device);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c6686adc",
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_tokenized.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "07122e49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 512, 768])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_batch = {\"attention_mask\": imdb_tokenized[\"train\"][:3][\"attention_mask\"].to(device),\n",
    "              \"input_ids\": imdb_tokenized[\"train\"][:3][\"input_ids\"].to(device)}\n",
    "\n",
    "with torch.inference_mode():\n",
    "    test_output = model(**test_batch)\n",
    "    \n",
    "test_output.last_hidden_state.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "083e61f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 768])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls_token_output = test_output.last_hidden_state[:, 0]\n",
    "cls_token_output.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "316d0450",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_output_embeddings(batch):\n",
    "    inputs = {key:tensor.to(device) for key,tensor in batch.items() if key != \"label\"}\n",
    "    with torch.inference_mode():\n",
    "        output = model(**inputs).last_hidden_state[:, 0]\n",
    "    return {\"features\": output.cpu().numpy()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2629aaa3",
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "883d3fa8144a49939b15c89991a946c6",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dc395698fd404642b330ebb3103b7666",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imdb_features = imdb_tokenized.map(get_output_embeddings, batched=True, batch_size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0fe91178",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.array(imdb_features[\"train\"][\"features\"])\n",
    "y_train = np.array(imdb_features[\"train\"][\"label\"])\n",
    "\n",
    "X_val = np.array(imdb_features[\"validation\"][\"features\"])\n",
    "y_val = np.array(imdb_features[\"validation\"][\"label\"])\n",
    "\n",
    "X_test = np.array(imdb_features[\"test\"][\"features\"])\n",
    "y_test = np.array(imdb_features[\"test\"][\"label\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e76e2e95-e9b3-4a54-b778-0bdcef59f098",
   "metadata": {},
   "source": [
    "# 4 Train Model on Embeddings (Extracted Features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "201a4329-7a91-4501-9c75-4d18f4646fa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy 1.0\n",
      "Validation accuracy 0.8408\n",
      "test accuracy 0.8324\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "print(\"Training accuracy\", rf.score(X_train, y_train))\n",
    "print(\"Validation accuracy\", rf.score(X_val, y_val))\n",
    "print(\"test accuracy\", rf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "81c31cf9-ec66-41a9-aa54-3e5b6ca33cf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy 0.8864857142857143\n",
      "Validation accuracy 0.883\n",
      "test accuracy 0.8794\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "rf = LogisticRegression(max_iter=1000)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "print(\"Training accuracy\", rf.score(X_train, y_train))\n",
    "print(\"Validation accuracy\", rf.score(X_val, y_val))\n",
    "print(\"test accuracy\", rf.score(X_test, y_test))"
   ]
  }
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